Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A closer look at the approximation capabilities of neural networks
Authors: Kai Fong Ernest Chong
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give a direct algebraic proof of the theorem. Furthermore we shall explicitly quantify the number of hidden units required for approximation. |
| Researcher Affiliation | Academia | Kai Fong Ernest Chong Information Systems Technology and Design (ISTD) pillar, Singapore University of Technology and Design, Singapore ernest EMAIL |
| Pseudocode | No | The paper is theoretical and does not present any algorithms or procedures in pseudocode format. |
| Open Source Code | No | The paper is theoretical and does not present any new software or code for release. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments requiring specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not involve computational experiments requiring specific software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup. |